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car-detection-bayes/utils/utils.py

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import glob
import os
import random
import shutil
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
import math
from . import torch_utils # , google_utils
matplotlib.rc('font', **{'size': 11})
# Set printoptions
torch.set_printoptions(linewidth=320, precision=5, profile='long')
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
# Prevent OpenCV from multithreading (to use PyTorch DataLoader)
cv2.setNumThreads(0)
def floatn(x, n=3): # format floats to n decimals
return float(format(x, '.%gf' % n))
def init_seeds(seed=0):
random.seed(seed)
np.random.seed(seed)
torch_utils.init_seeds(seed=seed)
def load_classes(path):
# Loads *.names file at 'path'
with open(path, 'r') as f:
names = f.read().split('\n')
return list(filter(None, names)) # filter removes empty strings (such as last line)
def labels_to_class_weights(labels, nc=80):
# Get class weights (inverse frequency) from training labels
if labels[0] is None: # no labels loaded
return torch.Tensor()
labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
classes = labels[:, 0].astype(np.int) # labels = [class xywh]
weights = np.bincount(classes, minlength=nc) # occurences per class
# Prepend gridpoint count (for uCE trianing)
# gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
# weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
weights[weights == 0] = 1 # replace empty bins with 1
weights = 1 / weights # number of targets per class
weights /= weights.sum() # normalize
return torch.from_numpy(weights)
def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
# Produces image weights based on class mAPs
n = len(labels)
class_counts = np.array([np.bincount(labels[i][:, 0].astype(np.int), minlength=nc) for i in range(n)])
image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
# index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
return image_weights
def coco_class_weights(): # frequency of each class in coco train2014
n = [187437, 4955, 30920, 6033, 3838, 4332, 3160, 7051, 7677, 9167, 1316, 1372, 833, 6757, 7355, 3302, 3776, 4671,
6769, 5706, 3908, 903, 3686, 3596, 6200, 7920, 8779, 4505, 4272, 1862, 4698, 1962, 4403, 6659, 2402, 2689,
4012, 4175, 3411, 17048, 5637, 14553, 3923, 5539, 4289, 10084, 7018, 4314, 3099, 4638, 4939, 5543, 2038, 4004,
5053, 4578, 27292, 4113, 5931, 2905, 11174, 2873, 4036, 3415, 1517, 4122, 1980, 4464, 1190, 2302, 156, 3933,
1877, 17630, 4337, 4624, 1075, 3468, 135, 1380]
weights = 1 / torch.Tensor(n)
weights /= weights.sum()
# with open('data/coco.names', 'r') as f:
# for k, v in zip(f.read().splitlines(), n):
# print('%20s: %g' % (k, v))
return weights
def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
# x = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
return x
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.03)
elif classname.find('BatchNorm2d') != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.03)
torch.nn.init.constant_(m.bias.data, 0.0)
def xyxy2xywh(x):
# Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
y[:, 0] = (x[:, 0] + x[:, 2]) / 2
y[:, 1] = (x[:, 1] + x[:, 3]) / 2
y[:, 2] = x[:, 2] - x[:, 0]
y[:, 3] = x[:, 3] - x[:, 1]
return y
def xywh2xyxy(x):
# Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2
y[:, 1] = x[:, 1] - x[:, 3] / 2
y[:, 2] = x[:, 0] + x[:, 2] / 2
y[:, 3] = x[:, 1] + x[:, 3] / 2
return y
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # not supplied, calculate
gain = max(img1_shape) / max(img0_shape) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
coords[:, [0, 2]] -= pad[0] # x padding
coords[:, [1, 3]] -= pad[1] # y padding
coords[:, :4] /= gain
clip_coords(coords, img0_shape)
return coords
def clip_coords(boxes, img_shape):
# Clip bounding xyxy bounding boxes to image shape (height, width)
boxes[:, [0, 2]] = boxes[:, [0, 2]].clamp(min=0, max=img_shape[1]) # clip x
boxes[:, [1, 3]] = boxes[:, [1, 3]].clamp(min=0, max=img_shape[0]) # clip y
def ap_per_class(tp, conf, pred_cls, target_cls):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
tp: True positives (list).
conf: Objectness value from 0-1 (list).
pred_cls: Predicted object classes (list).
target_cls: True object classes (list).
# Returns
The average precision as computed in py-faster-rcnn.
"""
# Sort by objectness
i = np.argsort(-conf)
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
# Find unique classes
unique_classes = np.unique(target_cls)
# Create Precision-Recall curve and compute AP for each class
ap, p, r = [], [], []
for c in unique_classes:
i = pred_cls == c
n_gt = (target_cls == c).sum() # Number of ground truth objects
n_p = i.sum() # Number of predicted objects
if n_p == 0 and n_gt == 0:
continue
elif n_p == 0 or n_gt == 0:
ap.append(0)
r.append(0)
p.append(0)
else:
# Accumulate FPs and TPs
fpc = (1 - tp[i]).cumsum()
tpc = (tp[i]).cumsum()
# Recall
recall = tpc / (n_gt + 1e-16) # recall curve
r.append(recall[-1])
# Precision
precision = tpc / (tpc + fpc) # precision curve
p.append(precision[-1])
# AP from recall-precision curve
ap.append(compute_ap(recall, precision))
# Plot
# fig, ax = plt.subplots(1, 1, figsize=(4, 4))
# ax.plot(np.concatenate(([0.], recall)), np.concatenate(([0.], precision)))
# ax.set_xlabel('YOLOv3-SPP')
# ax.set_xlabel('Recall')
# ax.set_ylabel('Precision')
# ax.set_xlim(0, 1)
# fig.tight_layout()
# fig.savefig('PR_curve.png', dpi=300)
# Compute F1 score (harmonic mean of precision and recall)
p, r, ap = np.array(p), np.array(r), np.array(ap)
f1 = 2 * p * r / (p + r + 1e-16)
return p, r, ap, f1, unique_classes.astype('int32')
def compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rbgirshick/py-faster-rcnn.
# Arguments
recall: The recall curve (list).
precision: The precision curve (list).
# Returns
The average precision as computed in py-faster-rcnn.
"""
# Append sentinel values to beginning and end
mrec = np.concatenate(([0.], recall, [min(recall[-1] + 1E-3, 1.)]))
mpre = np.concatenate(([0.], precision, [0.]))
# Compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# Integrate area under curve
method = 'interp' # methods: 'continuous', 'interp'
if method == 'interp':
x = np.linspace(0, 1, 101) # 101-point interp (COCO)
ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
else: # 'continuous'
i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
return ap
def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
box2 = box2.t()
# Get the coordinates of bounding boxes
if x1y1x2y2: # x1, y1, x2, y2 = box1
b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
else: # x, y, w, h = box1
b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
# Intersection area
inter_area = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
# Union Area
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
iou = inter_area / union_area # iou
if GIoU or DIoU or CIoU:
cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
if GIoU: # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
c_area = cw * ch + 1e-16 # convex area
return iou - (c_area - union_area) / c_area # GIoU
if DIoU or CIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
# convex diagonal squared
c2 = cw ** 2 + ch ** 2 + 1e-16
# centerpoint distance squared
rho2 = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2)) ** 2 / 4 + ((b2_y1 + b2_y2) - (b1_y1 + b1_y2)) ** 2 / 4
if DIoU:
return iou - rho2 / c2 # DIoU
elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
with torch.no_grad():
alpha = v / (1 - iou + v)
return iou - (rho2 / c2 + v * alpha) # CIoU
return iou
def wh_iou(box1, box2):
# Returns the IoU of wh1 to wh2. wh1 is 2, wh2 is nx2
box2 = box2.t()
# w, h = box1
w1, h1 = box1[0], box1[1]
w2, h2 = box2[0], box2[1]
# Intersection area
inter_area = torch.min(w1, w2) * torch.min(h1, h2)
# Union Area
union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
return inter_area / union_area # iou
class FocalLoss(nn.Module):
# Wraps focal loss around existing loss_fcn() https://arxiv.org/pdf/1708.02002.pdf
# i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=2.5)
def __init__(self, loss_fcn, gamma=0.5, alpha=1, reduction='mean'):
super(FocalLoss, self).__init__()
loss_fcn.reduction = 'none' # required to apply FL to each element
self.loss_fcn = loss_fcn
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
def forward(self, input, target):
loss = self.loss_fcn(input, target)
loss *= self.alpha * (1.000001 - torch.exp(-loss)) ** self.gamma # non-zero power for gradient stability
if self.reduction == 'mean':
return loss.mean()
elif self.reduction == 'sum':
return loss.sum()
else: # 'none'
return loss
def compute_loss(p, targets, model): # predictions, targets, model
ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
lcls, lbox, lobj = ft([0]), ft([0]), ft([0])
tcls, tbox, indices, anchor_vec = build_targets(model, targets)
h = model.hyp # hyperparameters
arc = model.arc # # (default, uCE, uBCE) detection architectures
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]))
BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]))
BCE = nn.BCEWithLogitsLoss()
CE = nn.CrossEntropyLoss() # weight=model.class_weights
if 'F' in arc: # add focal loss
g = h['fl_gamma']
BCEcls, BCEobj, BCE, CE = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g), FocalLoss(BCE, g), FocalLoss(CE, g)
# Compute losses
for i, pi in enumerate(p): # layer index, layer predictions
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros_like(pi[..., 0]) # target obj
# Compute losses
nb = len(b)
if nb: # number of targets
ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
tobj[b, a, gj, gi] = 1.0 # obj
# ps[:, 2:4] = torch.sigmoid(ps[:, 2:4]) # wh power loss (uncomment)
# GIoU
pxy = torch.sigmoid(ps[:, 0:2]) # pxy = pxy * s - (s - 1) / 2, s = 1.5 (scale_xy)
pbox = torch.cat((pxy, torch.exp(ps[:, 2:4]).clamp(max=1E4) * anchor_vec[i]), 1) # predicted box
giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
lbox += (1.0 - giou).mean() # giou loss
if 'default' in arc and model.nc > 1: # cls loss (only if multiple classes)
t = torch.zeros_like(ps[:, 5:]) # targets
t[range(nb), tcls[i]] = 1.0
lcls += BCEcls(ps[:, 5:], t) # BCE
# lcls += CE(ps[:, 5:], tcls[i]) # CE
# Instance-class weighting (use with reduction='none')
# nt = t.sum(0) + 1 # number of targets per class
# lcls += (BCEcls(ps[:, 5:], t) / nt).mean() * nt.mean() # v1
# lcls += (BCEcls(ps[:, 5:], t) / nt[tcls[i]].view(-1,1)).mean() * nt.mean() # v2
# Append targets to text file
# with open('targets.txt', 'a') as file:
# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
if 'default' in arc: # separate obj and cls
lobj += BCEobj(pi[..., 4], tobj) # obj loss
elif 'BCE' in arc: # unified BCE (80 classes)
t = torch.zeros_like(pi[..., 5:]) # targets
if nb:
t[b, a, gj, gi, tcls[i]] = 1.0
lobj += BCE(pi[..., 5:], t)
elif 'CE' in arc: # unified CE (1 background + 80 classes)
t = torch.zeros_like(pi[..., 0], dtype=torch.long) # targets
if nb:
t[b, a, gj, gi] = tcls[i] + 1
lcls += CE(pi[..., 4:].view(-1, model.nc + 1), t.view(-1))
lbox *= h['giou']
lobj *= h['obj']
lcls *= h['cls']
loss = lbox + lobj + lcls
return loss, torch.cat((lbox, lobj, lcls, loss)).detach()
def build_targets(model, targets):
# targets = [image, class, x, y, w, h]
nt = len(targets)
tcls, tbox, indices, av = [], [], [], []
multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
for i in model.yolo_layers:
# get number of grid points and anchor vec for this yolo layer
if multi_gpu:
ng, anchor_vec = model.module.module_list[i].ng, model.module.module_list[i].anchor_vec
else:
ng, anchor_vec = model.module_list[i].ng, model.module_list[i].anchor_vec
# iou of targets-anchors
t, a = targets, []
gwh = t[:, 4:6] * ng
if nt:
iou = torch.stack([wh_iou(x, gwh) for x in anchor_vec], 0)
use_best_anchor = False
if use_best_anchor:
iou, a = iou.max(0) # best iou and anchor
else: # use all anchors
na = len(anchor_vec) # number of anchors
a = torch.arange(na).view((-1, 1)).repeat([1, nt]).view(-1)
t = targets.repeat([na, 1])
gwh = gwh.repeat([na, 1])
iou = iou.view(-1) # use all ious
# reject anchors below iou_thres (OPTIONAL, increases P, lowers R)
reject = True
if reject:
j = iou > model.hyp['iou_t'] # iou threshold hyperparameter
t, a, gwh = t[j], a[j], gwh[j]
# Indices
b, c = t[:, :2].long().t() # target image, class
gxy = t[:, 2:4] * ng # grid x, y
gi, gj = gxy.long().t() # grid x, y indices
indices.append((b, a, gj, gi))
# GIoU
gxy -= gxy.floor() # xy
tbox.append(torch.cat((gxy, gwh), 1)) # xywh (grids)
av.append(anchor_vec[a]) # anchor vec
# Class
tcls.append(c)
if c.shape[0]: # if any targets
assert c.max() <= model.nc, 'Target classes exceed model classes'
return tcls, tbox, indices, av
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5):
"""
Removes detections with lower object confidence score than 'conf_thres'
Non-Maximum Suppression to further filter detections.
Returns detections with shape:
(x1, y1, x2, y2, object_conf, class_conf, class)
"""
min_wh, max_wh = 2, 30000 # (pixels) minimum and maximium box width and height
output = [None] * len(prediction)
for image_i, pred in enumerate(prediction):
# Experiment: Prior class size rejection
# x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
# a = w * h # area
# ar = w / (h + 1e-16) # aspect ratio
# n = len(w)
# log_w, log_h, log_a, log_ar = torch.log(w), torch.log(h), torch.log(a), torch.log(ar)
# shape_likelihood = np.zeros((n, 60), dtype=np.float32)
# x = np.concatenate((log_w.reshape(-1, 1), log_h.reshape(-1, 1)), 1)
# from scipy.stats import multivariate_normal
# for c in range(60):
# shape_likelihood[:, c] =
# multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])
# Multiply conf by class conf to get combined confidence
class_conf, class_pred = pred[:, 5:].max(1)
pred[:, 4] *= class_conf
# # Merge classes (optional)
# class_pred[(class_pred.view(-1,1) == torch.LongTensor([2, 3, 5, 6, 7]).view(1,-1)).any(1)] = 2
#
# # Remove classes (optional)
# pred[class_pred != 2, 4] = 0.0
# Select only suitable predictions
i = (pred[:, 4] > conf_thres) & (pred[:, 2:4] > min_wh).all(1) & (pred[:, 2:4] < max_wh).all(1) & \
torch.isfinite(pred).all(1)
pred = pred[i]
# If none are remaining => process next image
if len(pred) == 0:
continue
# Select predicted classes
class_conf = class_conf[i]
class_pred = class_pred[i].unsqueeze(1).float()
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
pred[:, :4] = xywh2xyxy(pred[:, :4])
# pred[:, 4] *= class_conf # improves mAP from 0.549 to 0.551
# Detections ordered as (x1y1x2y2, obj_conf, class_conf, class_pred)
pred = torch.cat((pred[:, :5], class_conf.unsqueeze(1), class_pred), 1)
# Get detections sorted by decreasing confidence scores
pred = pred[(-pred[:, 4]).argsort()]
det_max = []
nms_style = 'MERGE' # 'OR' (default), 'AND', 'MERGE' (experimental)
for c in pred[:, -1].unique():
dc = pred[pred[:, -1] == c] # select class c
n = len(dc)
if n == 1:
det_max.append(dc) # No NMS required if only 1 prediction
continue
elif n > 500:
dc = dc[:500] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117
# Non-maximum suppression
if nms_style == 'OR': # default
# METHOD1
# ind = list(range(len(dc)))
# while len(ind):
# j = ind[0]
# det_max.append(dc[j:j + 1]) # save highest conf detection
# reject = (bbox_iou(dc[j], dc[ind]) > nms_thres).nonzero()
# [ind.pop(i) for i in reversed(reject)]
# METHOD2
while dc.shape[0]:
det_max.append(dc[:1]) # save highest conf detection
if len(dc) == 1: # Stop if we're at the last detection
break
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
dc = dc[1:][iou < nms_thres] # remove ious > threshold
elif nms_style == 'AND': # requires overlap, single boxes erased
while len(dc) > 1:
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
if iou.max() > 0.5:
det_max.append(dc[:1])
dc = dc[1:][iou < nms_thres] # remove ious > threshold
elif nms_style == 'MERGE': # weighted mixture box
while len(dc):
if len(dc) == 1:
det_max.append(dc)
break
i = bbox_iou(dc[0], dc) > nms_thres # iou with other boxes
weights = dc[i, 4:5]
dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()
det_max.append(dc[:1])
dc = dc[i == 0]
elif nms_style == 'SOFT': # soft-NMS https://arxiv.org/abs/1704.04503
sigma = 0.5 # soft-nms sigma parameter
while len(dc):
if len(dc) == 1:
det_max.append(dc)
break
det_max.append(dc[:1])
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
dc = dc[1:]
dc[:, 4] *= torch.exp(-iou ** 2 / sigma) # decay confidences
# dc = dc[dc[:, 4] > nms_thres] # new line per https://github.com/ultralytics/yolov3/issues/362
if len(det_max):
det_max = torch.cat(det_max) # concatenate
output[image_i] = det_max[(-det_max[:, 4]).argsort()] # sort
return output
def get_yolo_layers(model):
bool_vec = [x['type'] == 'yolo' for x in model.module_defs]
return [i for i, x in enumerate(bool_vec) if x] # [82, 94, 106] for yolov3
def print_model_biases(model):
# prints the bias neurons preceding each yolo layer
print('\nModel Bias Summary (per output layer):')
multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
for l in model.yolo_layers: # print pretrained biases
if multi_gpu:
na = model.module.module_list[l].na # number of anchors
b = model.module.module_list[l - 1][0].bias.view(na, -1) # bias 3x85
else:
na = model.module_list[l].na
b = model.module_list[l - 1][0].bias.view(na, -1) # bias 3x85
print('regression: %5.2f+/-%-5.2f ' % (b[:, :4].mean(), b[:, :4].std()),
'objectness: %5.2f+/-%-5.2f ' % (b[:, 4].mean(), b[:, 4].std()),
'classification: %5.2f+/-%-5.2f' % (b[:, 5:].mean(), b[:, 5:].std()))
def strip_optimizer(f='weights/last.pt'): # from utils.utils import *; strip_optimizer()
# Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
x = torch.load(f, map_location=torch.device('cpu'))
x['optimizer'] = None
# x['training_results'] = None # uncomment to create a backbone
# x['epoch'] = -1 # uncomment to create a backbone
torch.save(x, f)
def create_backbone(f='weights/last.pt'): # from utils.utils import *; create_backbone()
# create a backbone from a *.pt file
x = torch.load(f, map_location=torch.device('cpu'))
x['optimizer'] = None
x['training_results'] = None
x['epoch'] = -1
for p in x['model'].values():
try:
p.requires_grad = True
except:
pass
torch.save(x, 'weights/backbone.pt')
def coco_class_count(path='../coco/labels/train2014/'):
# Histogram of occurrences per class
nc = 80 # number classes
x = np.zeros(nc, dtype='int32')
files = sorted(glob.glob('%s/*.*' % path))
for i, file in enumerate(files):
labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
x += np.bincount(labels[:, 0].astype('int32'), minlength=nc)
print(i, len(files))
def coco_only_people(path='../coco/labels/val2014/'):
# Find images with only people
files = sorted(glob.glob('%s/*.*' % path))
for i, file in enumerate(files):
labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
if all(labels[:, 0] == 0):
print(labels.shape[0], file)
def select_best_evolve(path='evolve*.txt'): # from utils.utils import *; select_best_evolve()
# Find best evolved mutation
for file in sorted(glob.glob(path)):
x = np.loadtxt(file, dtype=np.float32, ndmin=2)
print(file, x[fitness(x).argmax()])
def crop_images_random(path='../images/', scale=0.50): # from utils.utils import *; crop_images_random()
# crops images into random squares up to scale fraction
# WARNING: overwrites images!
for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
img = cv2.imread(file) # BGR
if img is not None:
h, w = img.shape[:2]
# create random mask
a = 30 # minimum size (pixels)
mask_h = random.randint(a, int(max(a, h * scale))) # mask height
mask_w = mask_h # mask width
# box
xmin = max(0, random.randint(0, w) - mask_w // 2)
ymin = max(0, random.randint(0, h) - mask_h // 2)
xmax = min(w, xmin + mask_w)
ymax = min(h, ymin + mask_h)
# apply random color mask
cv2.imwrite(file, img[ymin:ymax, xmin:xmax])
def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
# Makes single-class coco datasets. from utils.utils import *; coco_single_class_labels()
if os.path.exists('new/'):
shutil.rmtree('new/') # delete output folder
os.makedirs('new/') # make new output folder
os.makedirs('new/labels/')
os.makedirs('new/images/')
for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
with open(file, 'r') as f:
labels = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
i = labels[:, 0] == label_class
if any(i):
img_file = file.replace('labels', 'images').replace('txt', 'jpg')
labels[:, 0] = 0 # reset class to 0
with open('new/images.txt', 'a') as f: # add image to dataset list
f.write(img_file + '\n')
with open('new/labels/' + Path(file).name, 'a') as f: # write label
for l in labels[i]:
f.write('%g %.6f %.6f %.6f %.6f\n' % tuple(l))
shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
def kmeans_targets(path='../coco/trainvalno5k.txt', n=9, img_size=416): # from utils.utils import *; kmeans_targets()
# Produces a list of target kmeans suitable for use in *.cfg files
from utils.datasets import LoadImagesAndLabels
from scipy import cluster
# Get label wh
dataset = LoadImagesAndLabels(path, augment=True, rect=True, cache_labels=True)
for s, l in zip(dataset.shapes, dataset.labels):
l[:, [1, 3]] *= s[0] # normalized to pixels
l[:, [2, 4]] *= s[1]
l[:, 1:] *= img_size / max(s) * random.uniform(0.5, 1.5) # nominal img_size for training
wh = np.concatenate(dataset.labels, 0)[:, 3:5] # wh from cxywh
# Kmeans calculation
k, dist = cluster.vq.kmeans(wh, n) # points, mean distance
k = k[np.argsort(k.prod(1))] # sort small to large
# # Plot
# k, d = [None] * 20, [None] * 20
# for i in tqdm(range(1, 21)):
# k[i-1], d[i-1] = cluster.vq.kmeans(wh, i) # points, mean distance
# fig, ax = plt.subplots(1, 2, figsize=(14, 7))
# ax = ax.ravel()
# ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
# Measure IoUs
iou = torch.stack([wh_iou(torch.Tensor(wh).T, torch.Tensor(x).T) for x in k], 0)
biou = iou.max(0)[0] # closest anchor IoU
print('Best possible recall: %.3f' % (biou > 0.2635).float().mean()) # BPR (best possible recall)
# Print
print('kmeans anchors (n=%g, img_size=%g, IoU=%.2f/%.2f/%.2f-min/mean/best): ' %
(n, img_size, biou.min(), iou.mean(), biou.mean()), end='')
for i, x in enumerate(k):
print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
# Plot
# plt.hist(biou.numpy().ravel(), 100)
def print_mutation(hyp, results, bucket=''):
# Print mutation results to evolve.txt (for use with train.py --evolve)
a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
c = '%10.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
if bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % bucket) # download evolve.txt
with open('evolve.txt', 'a') as f: # append result
f.write(c + b + '\n')
x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
np.savetxt('evolve.txt', x[np.argsort(-fitness(x))], '%10.3g') # save sort by fitness
if bucket:
os.system('gsutil cp evolve.txt gs://%s' % bucket) # upload evolve.txt
def apply_classifier(x, model, img, im0):
# applies a second stage classifier to yolo outputs
for i, d in enumerate(x): # per image
if d is not None and len(d):
d = d.clone()
# Reshape and pad cutouts
b = xyxy2xywh(d[:, :4]) # boxes
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
d[:, :4] = xywh2xyxy(b).long()
# Rescale boxes from img_size to im0 size
scale_coords(img.shape[2:], d[:, :4], im0.shape)
# Classes
pred_cls1 = d[:, 6].long()
ims = []
for j, a in enumerate(d): # per item
cutout = im0[int(a[1]):int(a[3]), int(a[0]):int(a[2])]
im = cv2.resize(cutout, (224, 224)) # BGR
# cv2.imwrite('test%i.jpg' % j, cutout)
im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
im /= 255.0 # 0 - 255 to 0.0 - 1.0
ims.append(im)
pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction
x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections
return x
def fitness(x):
# Returns fitness (for use with results.txt or evolve.txt)
return x[:, 2] * 0.8 + x[:, 3] * 0.2 # weighted mAP and F1 combination
# Plotting functions ---------------------------------------------------------------------------------------------------
def plot_one_box(x, img, color=None, label=None, line_thickness=None):
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1) # filled
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
# Compares the two methods for width-height anchor multiplication
# https://github.com/ultralytics/yolov3/issues/168
x = np.arange(-4.0, 4.0, .1)
ya = np.exp(x)
yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
fig = plt.figure(figsize=(6, 3), dpi=150)
plt.plot(x, ya, '.-', label='yolo method')
plt.plot(x, yb ** 2, '.-', label='^2 power method')
plt.plot(x, yb ** 2.5, '.-', label='^2.5 power method')
plt.xlim(left=-4, right=4)
plt.ylim(bottom=0, top=6)
plt.xlabel('input')
plt.ylabel('output')
plt.legend()
fig.tight_layout()
fig.savefig('comparison.png', dpi=200)
def plot_images(imgs, targets, paths=None, fname='images.jpg'):
# Plots training images overlaid with targets
imgs = imgs.cpu().numpy()
targets = targets.cpu().numpy()
# targets = targets[targets[:, 1] == 21] # plot only one class
fig = plt.figure(figsize=(10, 10))
bs, _, h, w = imgs.shape # batch size, _, height, width
bs = min(bs, 16) # limit plot to 16 images
ns = np.ceil(bs ** 0.5) # number of subplots
for i in range(bs):
boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
boxes[[0, 2]] *= w
boxes[[1, 3]] *= h
plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
plt.axis('off')
if paths is not None:
s = Path(paths[i]).name
plt.title(s[:min(len(s), 40)], fontdict={'size': 8}) # limit to 40 characters
fig.tight_layout()
fig.savefig(fname, dpi=200)
plt.close()
def plot_test_txt(): # from utils.utils import *; plot_test()
# Plot test.txt histograms
x = np.loadtxt('test.txt', dtype=np.float32)
box = xyxy2xywh(x[:, :4])
cx, cy = box[:, 0], box[:, 1]
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
ax.set_aspect('equal')
fig.tight_layout()
plt.savefig('hist2d.jpg', dpi=300)
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0].hist(cx, bins=600)
ax[1].hist(cy, bins=600)
fig.tight_layout()
plt.savefig('hist1d.jpg', dpi=200)
def plot_targets_txt(): # from utils.utils import *; plot_targets_txt()
# Plot test.txt histograms
x = np.loadtxt('targets.txt', dtype=np.float32)
x = x.T
s = ['x targets', 'y targets', 'width targets', 'height targets']
fig, ax = plt.subplots(2, 2, figsize=(8, 8))
ax = ax.ravel()
for i in range(4):
ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
ax[i].legend()
ax[i].set_title(s[i])
fig.tight_layout()
plt.savefig('targets.jpg', dpi=200)
def plot_evolution_results(hyp): # from utils.utils import *; plot_evolution_results(hyp)
# Plot hyperparameter evolution results in evolve.txt
x = np.loadtxt('evolve.txt', ndmin=2)
f = fitness(x)
weights = (f - f.min()) ** 2 # for weighted results
fig = plt.figure(figsize=(12, 10))
matplotlib.rc('font', **{'size': 8})
for i, (k, v) in enumerate(hyp.items()):
y = x[:, i + 7]
# mu = (y * weights).sum() / weights.sum() # best weighted result
mu = y[f.argmax()] # best single result
plt.subplot(4, 5, i + 1)
plt.plot(mu, f.max(), 'o', markersize=10)
plt.plot(y, f, '.')
plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters
print('%15s: %.3g' % (k, mu))
fig.tight_layout()
plt.savefig('evolve.png', dpi=200)
def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_results_overlay()
# Plot training results files 'results*.txt', overlaying train and val losses
s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'F1'] # legends
t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
n = results.shape[1] # number of rows
x = range(start, min(stop, n) if stop else n)
fig, ax = plt.subplots(1, 5, figsize=(14, 3.5))
ax = ax.ravel()
for i in range(5):
for j in [i, i + 5]:
y = results[j, x]
if i in [0, 1, 2]:
y[y == 0] = np.nan # dont show zero loss values
ax[i].plot(x, y, marker='.', label=s[j])
ax[i].set_title(t[i])
ax[i].legend()
ax[i].set_ylabel(f) if i == 0 else None # add filename
fig.tight_layout()
fig.savefig(f.replace('.txt', '.png'), dpi=200)
def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
# Plot training results files 'results*.txt'
fig, ax = plt.subplots(2, 5, figsize=(14, 7))
ax = ax.ravel()
s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
'val GIoU', 'val Objectness', 'val Classification', 'mAP@0.5', 'F1']
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
n = results.shape[1] # number of rows
x = range(start, min(stop, n) if stop else n)
for i in range(10):
y = results[i, x]
if i in [0, 1, 2, 5, 6, 7]:
y[y == 0] = np.nan # dont show zero loss values
ax[i].plot(x, y, marker='.', label=f.replace('.txt', ''))
ax[i].set_title(s[i])
if i in [5, 6, 7]: # share train and val loss y axes
ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
fig.tight_layout()
ax[1].legend()
fig.savefig('results.png', dpi=200)
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